scholarly journals Bayesian Networks Optimization Based on Induction Learning Techniques

2008 ◽  
pp. 439-443 ◽  
Author(s):  
Paola Britos ◽  
Pablo Felgaer ◽  
Ramon Garcia-Martinez
Keyword(s):  
Bayesian Networks ◽  
Learning Techniques

ANOMALY DETECTION IN VESSEL TRACKING – A BAYESIAN NETWORKS (BNs) APPROACH

2021 ◽  
Vol 157 (A3) ◽  
Author(s):  
D Handayani ◽  
W Sediono ◽  
A Shah
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Expert Knowledge ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Identification System ◽  
International Convention ◽  
Learning Techniques ◽  
Reporting Data ◽  
Vessel Anomaly

The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour.

Dialogue act recognition under uncertainty using Bayesian networks

2007 ◽  
Vol 13 (4) ◽  
pp. 287-316 ◽  
Author(s):  
SIMON KEIZER ◽  
RIEKS OP DEN AKKER
Keyword(s):  
Bayesian Networks ◽  
Language Processing ◽  
General Framework ◽  
Probabilistic Models ◽  
Machine Learning Techniques ◽  
Dialogue Acts ◽  
Learning Techniques ◽  
Dialogue Modelling ◽  
Context Features ◽  
Modelling Process

AbstractIn this paper we discuss the task of dialogue act recognition as a part of interpreting user utterances in context. To deal with the uncertainty that is inherent in natural language processing in general and dialogue act recognition in particular we use machine learning techniques to train classifiers from corpus data. These classifiers make use of both lexical features of the (Dutch) keyboard-typed utterances in the corpus used, and context features in the form of dialogue acts of previous utterances. In particular, we consider probabilistic models in the form of Bayesian networks to be proposed as a more general framework for dealing with uncertainty in the dialogue modelling process.

scholarly journals Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Gonzalo A. Ruz ◽  
Pamela Araya-Díaz
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Classification Problem ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Bayesian Network Classifiers ◽  
Tree Construction ◽  
Learning Techniques ◽  
Vector Machines

Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. We apply Bayesian network classifiers to the facial biotype classification problem, an important stage during orthodontic treatment planning. For this, we present adaptations of classical Bayesian networks classifiers to handle continuous attributes; also, we propose an incremental tree construction procedure for tree like Bayesian network classifiers. We evaluate the performance of the proposed adaptations and compare them with other continuous Bayesian network classifiers approaches as well as support vector machines. The results under the classification performance measures, accuracy and kappa, showed the effectiveness of the continuous Bayesian network classifiers, especially for the case when a reduced number of attributes were used. Additionally, the resulting networks allowed visualizing the probability relations amongst the attributes under this classification problem, a useful tool for decision-making for orthodontists.

PREDICTION IN HEALTH DOMAIN USING BAYESIAN NETWORKS OPTIMIZATION BASED ON INDUCTION LEARNING TECHNIQUES

2006 ◽  
Vol 17 (03) ◽  
pp. 447-455 ◽  
Author(s):  
PABLO FELGAER ◽  
PAOLA BRITOS ◽  
RAMÓN GARCÍA-MARTÍNEZ
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Hybrid Method ◽  
Learning Process ◽  
Directed Acyclic Graph ◽  
Learning Method ◽  
Automatic Learning ◽  
Acyclic Graph ◽  
Health Domain ◽  
Learning Techniques

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

scholarly journals Knowledge Management for Open Innovation: Bayesian Networks through Machine Learning

2021 ◽  
Vol 7 (1) ◽  
pp. 40
Author(s):  
Antonia Terán-Bustamante ◽  
Antonieta Martínez-Velasco ◽  
Griselda Dávila-Aragón
Keyword(s):  
Machine Learning ◽  
Knowledge Management ◽  
Bayesian Networks ◽  
Business Strategy ◽  
Machine Learning Techniques ◽  
Global Business ◽  
Adequate Knowledge ◽  
Learning Techniques ◽  
Relevant Factors

Knowledge management within organizations allows to support a global business strategy and represents a systemic and organized attempt to use knowledge within an organization to improve its performance. The objective of this research is to study and analyze knowledge management through Bayesian networks with machine learning techniques, for which a model is made to identify and quantify the various factors that affect the correct management of knowledge in an organization, allowing you to generate value. As a case study, a technology-based services company in Mexico City is analyzed. The evidence found shows the optimal and non-optimal management of knowledge management, and its various factors, through the causality of the variables, allowing us to more adequately capture the interrelationship to manage it. The results show that the most relevant factors for having adequate knowledge management are information management, relational capital, intellectual capital, quality and risk management, and technology assimilation.

scholarly journals REQUIREMENT RISK LEVEL FORECAST USING BAYESIAN NETWORKS CLASSIFIERS

2011 ◽  
Vol 21 (02) ◽  
pp. 167-190 ◽  
Author(s):  
ISABEL MARÍA DEL ÁGUILA ◽  
JOSÉ DEL SAGRADO
Keyword(s):  
Bayesian Networks ◽  
Requirement Engineering ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Risk Level ◽  
Software Project ◽  
Development Activity ◽  
Learning Techniques ◽  
Risks Assessment ◽  
Requirement Specifications

Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering.

Anomaly Detection in Vessel Tracking – A Bayesian Networks (BNs) Approach

2015 ◽  
Vol 157 (A3) ◽  
pp. 145-152
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Expert Knowledge ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Identification System ◽  
International Convention ◽  
Learning Techniques ◽  
Reporting Data ◽  
Vessel Anomaly

"The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour."

The Use of Prescription Drugs and Drugs of Abuse for Neuroenhancement in Europe

2015 ◽  
Vol 20 (3) ◽  
pp. 155-166 ◽  
Author(s):  
Larissa J. Maier ◽  
Michael P. Schaub
Keyword(s):  
Prescription Drugs ◽  
Illicit Drugs ◽  
Adverse Reactions ◽  
Low Dose ◽  
Drugs Of Abuse ◽  
Mental Performance ◽  
Or Education ◽  
Learning Techniques ◽  
Monitoring The Future ◽  
Definition Of

Abstract. Pharmacological neuroenhancement, defined as the misuse of prescription drugs, illicit drugs, or alcohol for the purpose of enhancing cognition, mood, or prosocial behavior, is not widespread in Europe – nevertheless, it does occur. Thus far, no drug has been proven as safe and effective for cognitive enhancement in otherwise healthy individuals. European studies have investigated the misuse of prescription and illicit stimulants to increase cognitive performance as well as the use of tranquilizers, alcohol, and cannabis to cope with stress related to work or education. Young people in educational settings report pharmacological neuroenhancement more frequently than those in other settings. Although the regular use of drugs for neuroenhancement is not common in Europe, the irregular and low-dose usage of neuroenhancers might cause adverse reactions. Previous studies have revealed that obtaining adequate amounts of sleep and using successful learning techniques effectively improve mental performance, whereas pharmacological neuroenhancement is associated with ambiguous effects. Therefore, non-substance-related alternatives should be promoted to cope with stressful situations. This paper reviews the recent research on pharmacological neuroenhancement in Europe, develops a clear definition of the substances used, and formulates recommendations for practitioners regarding how to react to requests for neuroenhancement drug prescriptions. We conclude that monitoring the future development of pharmacological neuroenhancement in Europe is important to provide effective preventive measures when required. Furthermore, substance use to cope with stress related to work or education should be studied in depth because it is likely more prevalent and dangerous than direct neuroenhancement.

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